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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.
Topic 2	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 3	Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.
Topic 4	ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

Databricks Certified Machine Learning Associate Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
A machine learning engineer has grown tired of needing to install the MLflow Python library on each of their clusters. They ask a senior machine learning engineer how their notebooks can load the MLflow library without installing it each time. The senior machine learning engineer suggests that they use Databricks Runtime for Machine Learning.
Which of the following approaches describes how the machine learning engineer can begin using Databricks Runtime for Machine Learning?

A. They can set the runtime-version variable in their Spark session to "ml".
B. They can select a Databricks Runtime ML version from the Databricks Runtime Version dropdown when creating their clusters.
C. They can check the Databricks Runtime ML box when creating their clusters.
D. They can add a line enabling Databricks Runtime ML in their init script when creating their clusters.

Answer: B

Explanation:
The Databricks Runtime for Machine Learning includes pre-installed packages and libraries essential for machine learning and deep learning, including MLflow. To use it, the machine learning engineer can simply select an appropriate Databricks Runtime ML version from the "Databricks Runtime Version" dropdown menu while creating their cluster. This selection ensures that all necessary machine learning libraries, including MLflow, are pre-installed and ready for use, avoiding the need to manually install them each time.
Reference
Databricks documentation on creating clusters: https://docs.databricks.com/clusters/create.html

NEW QUESTION # 11
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?

A. One-hot encoding is dependent on the target variable's values which differ for each apaplication.
B. One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
C. One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
D. One-hot encoding is not a common strategy for representing categorical feature variables numerically.

Answer: C

Explanation:
The suggestion not to one-hot encode categorical feature variables within the feature repository is justified because one-hot encoding can be problematic for some machine learning algorithms. Specifically, one-hot encoding increases the dimensionality of the data, which can be computationally expensive and may lead to issues such as multicollinearity and overfitting. Additionally, some algorithms, such as tree-based methods, can handle categorical variables directly without requiring one-hot encoding.
Reference:
Databricks documentation on feature engineering: Feature Engineering

NEW QUESTION # 12
A machine learning engineer is using the following code block to scale the inference of a single-node model on a Spark DataFrame with one million records:

Assuming the default Spark configuration is in place, which of the following is a benefit of using an Iterator?

A. The model only needs to be loaded once per executor rather than once per batch during the inference process
B. The model will be limited to a single executor preventing the data from being distributed
C. The data will be limited to a single executor preventing the model from being loaded multiple times
D. The data will be distributed across multiple executors during the inference process

Answer: A

Explanation:
Using an iterator in the pandas_udf ensures that the model only needs to be loaded once per executor rather than once per batch. This approach reduces the overhead associated with repeatedly loading the model during the inference process, leading to more efficient and faster predictions. The data will be distributed across multiple executors, but each executor will load the model only once, optimizing the inference process.
Reference:
Databricks documentation on pandas UDFs: Pandas UDFs

NEW QUESTION # 13
A data scientist has written a data cleaning notebook that utilizes the pandas library, but their colleague has suggested that they refactor their notebook to scale with big data.
Which of the following approaches can the data scientist take to spend the least amount of time refactoring their notebook to scale with big data?

A. They can refactor their notebook to utilize the pandas API on Spark.
B. They can refactor their notebook to use the Scala Dataset API.
C. They can refactor their notebook to use the PySpark DataFrame API.
D. They can refactor their notebook to use Spark SQL.
E. They can refactor their notebook to process the data in parallel.

Answer: A

Explanation:
The data scientist can refactor their notebook to utilize the pandas API on Spark (now known as pandas on Spark, formerly Koalas). This allows for the least amount of changes to the existing pandas-based code while scaling to handle big data using Spark's distributed computing capabilities. pandas on Spark provides a similar API to pandas, making the transition smoother and faster compared to completely rewriting the code to use PySpark DataFrame API, Scala Dataset API, or Spark SQL.
Reference:
Databricks documentation on pandas API on Spark (formerly Koalas).

NEW QUESTION # 14
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

A. pandas API on Spark DataFrames are more performant than Spark DataFrames
B. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
C. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
D. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

Answer: D

Explanation:
The pandas API on Spark DataFrames are made up of Spark DataFrames with additional metadata. The pandas API on Spark aims to provide the pandas-like experience with the scalability and distributed nature of Spark. It allows users to work with pandas functions on large datasets by leveraging Spark's underlying capabilities.
Reference:
Databricks documentation on pandas API on Spark: pandas API on Spark

NEW QUESTION # 15
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NEW DATABRICKS DATABRICKS-MACHINE-LEARNING-ASSOCIATE TEST DISCOUNT & DATABRICKS-MACHINE-LEARNING-ASSOCIATE PDF PASS LEADER

New Databricks Databricks-Machine-Learning-Associate Test Discount & Databricks-Machine-Learning-Associate Pdf Pass Leader